BAM Transitions 2 Research Grant (£6,000).

Small and medium sized enterprises (SMEs) face rapid innovation cycles and fierce competition. AI – a system’s ability to correctly interpret external data, learn from it, and apply those learnings to achieve specific goals through adaptive
processes (Kaplan & Haenlein, 2019, p. 17) – provides many opportunities to manage these challenges successfully. Its analytical power holds promise to make strategic decisions faster, more accurate, reproducible, and cost-effective. AI-driven analytics can help forecast market trends, predict equipment failures, and streamline manufacturing processes. Preliminary evidence suggests that AI-augmented strategic decision-making increases decision trustworthiness and quality (Keding & Meissner, 2021). Despite these benefits, effective real-world strategic decision-making still requires human theory-based causal logic of engineers, technicians, and business leaders (Felin & Holweg, 2024).

Strategic decisions are ill-structured, characterized by limited, incomplete, or ambiguous information, unclear selection criteria, transparency requirements, and ethical or social considerations. In such cases, AI must be complemented by
managerial judgement (Shrestha et al., 2019; Vincent, 2021). While there is consensus that AI-augmented strategic decision-making is the future, little is known about how to organize it effectively. By exploring the interplay between AI-driven analytics and human judgment in SMEs, this research provides actionable insights into how to effectively organise AI augmented strategic decision-making.